A Data-driven Approach for Automated Quality Concern Extraction from App Reviews

User Review on App Distribution Platforms such as the Google Play Store, provide vital feedback on the software and offer several information-rich attributes such as user experience, performance, security and software reliability. However, owing to their inherently unstructured nature, informal tone, and the vague opinions, it becomes difficult to manually extract such information from App Reviews. Over the past few years, several Automated solutions using traditional machine learning (ML) and Deep learning (DL) approaches have been proposed. However, these solutions have significant methodological and scope-centric limitations including insufficient deep context understanding, and dependency on hand-crafted features, resulting in limited effectiveness in multi-label classification scenarios. This research aims at automating quality concern extraction from mobile app reviews using transformer-based language models. We benchmark mainstream Transformer-based language models against classical ML/DL baselines to highlight their relative advantages in context-aware multi-label classification. The proposed approach aims at reducing the reliance on manual feature engineering by leveraging self-attention mechanism and contextual embeddings to enhance semantic understanding of the reviews. Five selected quality concerns as part of ISO 25010 standard are targeted in this study. An annotated dataset of 20,000 real-world app reviews is used for the evaluation for performance evaluation of the proposed approach against precision, recall and F1-score. Through comprehensive empirical evaluation, the study validates the effectiveness and practicality of state-of-the-art transformer-based language models for automated extraction of software quality concerns.